Challenges

For parallelism to be added to a single-threaded task, the task must be able to be broken into sufficiently-large parts and executed independently. (If the sub-parts are too small, the overhead of doing parallelism overwhelms the benefits of parallelism.) Unfortunately, unlike a GUI application, the Postgres backend executes a query by performing many small tasks that must be executed in sequence, e.g. parser, planner, executor.

This means that databases allow parallelism only in limited situations, mostly for large queries that can become CPU or I/O bound. For example, it is unlikely that selecting a row based on a primary key would benefit from parallelism. In contrast, large queries can often benefit from parallelism.

Another challenge is returning data from the helper process/thread. For something like SUM(), it is easy, but passing a large volume of data back can be complex.

Parallelism has its own costs so there will need to be a way to control when parallel execution is used.

Would we need a job acceptance system to be able to balance incoming requests across the finite CPU resources available?

Benefits

There are three possible benefits of parallelism:

using multiple CPUs

using multiple I/O channels (for sequential and random I/O)

using multiple CPUs and I/O channels

Approaches

There are several methods to add parallelism:

use fork (or a thread on Windows) and only call libc and parallel-specific functions to do parallel computation or I/O. This avoids the problem of trying to make the existing backend code thread-safe. Do we need to wait until we can share a transaction among back-end processes?

same as above, but modify some existing backend modules to be fork/thread-safe, with or without shared memory access; this might allow entire executor node trees to be run in parallel

create full backends that can execute parts of a query in parallel and return results

An initial approach might start by modifying individual plan nodes to run in parallel in the executor. Eventually we'd need to educate the planner and optimizer about how to model parallelizing queries.